Recent progress in neural rendering has recaptured the research communities interest
in Fourier Features [1]}, [2]}
as inputs to coordinate MLPs.
We construct multi-dimensional MLPs with Fourier Features (FF) that take the usual \(b\) 
and in addition the perpendicular \(d_{\perp }\)  and parallel distance \(d_{\parallel }\) 
as inputs. The first operation on these three inputs is a transformation to Fourier
Features. In particular, we use three one-dimensional feature transformations to 64
Fourier Features each. The complete MLP is:
\(\textrm {MLP}(b, d_{\perp }, d_{\parallel })= \textrm {Softplus} \;\; L_{1\times 64} \;\; \big [ \textrm {Softplus} \;\;L_{64\times 64} \big ]^3 \;\; \textrm {Softplus} \;\; L_{64\times 192} \;\;\textrm {FF}_{192 \times 3} \;\;\begin{bmatrix}b \\d_{\perp } \\d_{\parallel }\end{bmatrix} \;\;.\) 
